第十章 统计回归模型 10.1 牙膏的销售量 10.2 软件开发人员的薪金 10.3 酶促反应 10.4 投资额与国民生产总值和物价指数 10.5 教学评估 10.6 冠心病与年龄
Jan 15, 2016
10.1 10.2 10.3 10.4 10.5 10.6
. . . . . ,.
10.1 ;. 30 .
y ~x1~x2~x1, x2~(, ) y~ 0, 1 , 2 , 3 ~ ~
MATLAB [b,bint,r,rint,stats]=regress(y,x,alpha) alpha(,0.05) b~ bint~b r ~y-xb rint~r Stats~ R2,F, p,s2 y~n y,x1,x2
y90.54% FF p=0.05 2() x2y x22 x2
x1=x3-x4x3x4x1=0.2x2=6.5 [7.82308.7636]95% x3=3.9x4=3.795% 7.83203.7 29
x1x2y
[7.82308.7636] [7.89538.7592] x1=0.2x2=6.5
x2=6.5x1=0.2
x1=0.1 x1=0.3 x26
MATLABrstool
, , (). (MATLAB). : R2,F, p, s2, . , . .
10.2 ~ ~ 1=,0=~ 1=2=3=...
y~ x1 ~x2 = 1~ x2 = 0~ 1=2=3=. a0, a1, , a4
R2,F, p 1546 6883 2994 148 a4!
,,. 3,6.x2x3, x4 .
x2x3, x4R2,F,, (33)!
R2: 0.9567 0.99880.9998F226 554 36701 s2: 104 3104 4103
6(0x3=1, x4=0 x3=0, x4=1 x3=0, x4=0 x1= 0 x2 = 1~ x2 = 0~ . .
()0-10-11. . .650-1.
10.3 . . . .
(ppm)0.020.060.110.220.561.107647971071231391591521912012072006751848698115131124144158160/
Michaelis-Menteny ~ , x ~ 1 , 2 ~ .
1 , 2
10-310-315.1072[3.5386 6.6758]20.2472[0.1757 0.3188]R2=0.8557 F=59.2975 p
xy 1/x1/x x1/x
[beta,R,J] = nlinfit (x,y,model,beta0) betaMATLAB x~y ~beta ~R ~J ~Jacobi model ~Mbeta0 ~ betaci =nlparci(beta,R,J) function y=f1(beta, x)y=beta(1)*x./(beta(2)+x);x= ; y= ;beta0=[195.8027 0.04841];[beta,R,J]=nlinfit(x,y,f1,beta0);betaci=nlparci(beta,R,J);beta, betaci beta0~
Export .ys= 10.9337nlintool
1212.6819[197.2029 228.1609]20.0641[0.0457 0.0826 ]
x1 x2 x2=1x2=0 1 1 2 2
nlinfit nlintools= 10.4000 22y()
1160.2802[145.8466 174.7137]20.0477[0.0304 0.0650 ]152.4035[32.4130 72.3941 ]20.0164[-0.0075 0.0403]
. s = 10.5851.
1166.6025[154.4886 178.7164]20.0580[0.0456 0.0703 ]142.0252[28.9419 55.1085]
.
((6747.34439.207842.73585.44465147.34439.207842.73585.44468489.28569.571084.73567.0478191190.83299.1484189.05748.8438201190.83299.1484189.05748.8438207200.968811.0447198.183710.1812200200.968811.0447198.183710.1812
R2 s.
10.4 ( GNP ) ( PI ) .GNPPI. 20
... ..
GNPt ~ yt ~ x1t~ GNP, x2t ~ 0, 1, 2 ~ t ~t
MATLAB s=12.7164 .R20.9908.
0322.7250[224.3386 421.1114]10.6185[0.4773 0.7596]2-859.4790[-1121.4757 -597.4823 ] R2= 0.9908 F= 919.8529 p
et~et-1 1, 3 2, 4 MATLABett
~ 0, 1, 2 ~ = 0> 0< 0 D-W ut ~t
D-WD-W ,dLdUDW
*0, 1 , 2 DW
DWold < dL etn=20, k=3, =0.05 dL=1.10, dU=1.54
snew= 9.8277 < sold=12.7164
*0163.4905[1265.4592 2005.2178]10.6990[0.5751 0.8247]2-1009.0333[-1235.9392 -782.1274]R2= 0.9772 F=342.8988 p
dU< DWnew < 4-dU etn=19, k=3, =0.05 dL=1.08, dU=1.53
et.
yt x1t x2t t=21 x1t =3312x2t=2.1938t yt-1=424.5
10.5 15. X1 ~X2 ~X3 ~X4 ~X5 ~X6 ~Y ~.Y X1~ X6.
X1X2X3X4X5X6Y2014.464.424.234.104.564.374.112244.113.823.293.603.993.823.384244.244.384.354.484.154.504.33
X1~ X6Y.YXX. . XS0 . S0XY, S0 S1 . S1X, S1 S2 . .
MATLABx~nk n, k y~n stepwise (x,y,inmodel,penter,premove) Inmodel~S0x penter~0.05 premove~0.10 .
MATLABstepwise (x,y) xX1~ X6, yY
MATLAB: Move x3 in, Move x1 in, Move x2 out
X1~ X6, Y (MATLABcorrcoef ): 1.0000 0.9008 0.6752 0.7361 0.2910 0.6471 0.8973 0.9008 1.0000 0.8504 0.7399 0.2775 0.8026 0.9363 0.6752 0.8504 1.0000 0.7499 0.0808 0.8490 0.9116 0.7361 0.7399 0.7499 1.0000 0.4370 0.7041 0.8219 0.2910 0.2775 0.0808 0.4370 1.0000 0.1872 0.1783 0.6471 0.8026 0.8490 0.7041 0.1872 1.0000 0.8246 0.8973 0.9363 0.9116 0.8219 0.1783 0.8246 1.0000 Y0.85X1, X2, X3 . X2X1, X3 0.85.X1, X2 ?
X11Y0.5, X31Y0.77.X1 ~X2 ~X3 ~X4 ~X5 ~X6 ~Y ~. . .
yx1yx2
ertong.m
10.6 , . , . . 100 ,,.
1200263505144176551253405044075551100691
x~, Y~ (Y=1~, Y=0~) ,01S-. ,.
20-2924.51010.130-34321520.1360-6964.51080.80100430.43
yxY Y 0, 1 ; y [0, 1] y[0,1]. 0,1, , .! Y().Y
Logit (x)~x(y)(x) ~ S-, [0,1] Logit (Logistic)
Logit : k(=8).xi~i, ni~, mi~, i=1,, k 0,1~ .
LogitMATLABglmfit b = glmfit(x, y, distr, link) [b,dev,stats] = glmfit(x, y, distr, link)x~(11).y~(distr =binomial, y: 1 , 2). distr ~(binomial,poisson ), normal .link ~logit,probit (logit). b~, dev~, stats~
[yhat, dylo, dyhi] = glmval(b, x, 'logit')0.5242
0-5.03821.086310.10500.0231
x( )(y)20-2924.50.10.0783[0.0282, 0.1992]60-6964.50.800.8501[0.6855, 0.9366]
Logitx2pval = 1 - chi2cdf(dev-dev2,1) =0.9371 Probit() (S-)
glmfitlogitprobit. Probit0.6529
x( )1Logit2Probit20-2924.50.10.07830.071560-6964.50.800.85010.8489
0-2.99330.601110.06240.0128
1 Odds~()(). x 1Odds()1OddskOdds
20 (103060
. LogitProbit, (), , . Logit